marching_cubes_hoppe.hpp
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00035 
00036 #ifndef PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
00037 #define PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
00038 
00039 #include <pcl/surface/marching_cubes_hoppe.h>
00040 #include <pcl/common/common.h>
00041 #include <pcl/common/vector_average.h>
00042 #include <pcl/Vertices.h>
00043 #include <pcl/kdtree/kdtree_flann.h>
00044 
00046 template <typename PointNT>
00047 pcl::MarchingCubesHoppe<PointNT>::MarchingCubesHoppe ()
00048   : MarchingCubes<PointNT> ()
00049 {
00050 }
00051 
00053 template <typename PointNT>
00054 pcl::MarchingCubesHoppe<PointNT>::~MarchingCubesHoppe ()
00055 {
00056 }
00057 
00058 
00060 template <typename PointNT> void
00061 pcl::MarchingCubesHoppe<PointNT>::voxelizeData ()
00062 {
00063   for (int x = 0; x < res_x_; ++x)
00064     for (int y = 0; y < res_y_; ++y)
00065       for (int z = 0; z < res_z_; ++z)
00066       {
00067         std::vector<int> nn_indices;
00068         std::vector<float> nn_sqr_dists;
00069 
00070         Eigen::Vector3f point;
00071         point[0] = min_p_[0] + (max_p_[0] - min_p_[0]) * x / res_x_;
00072         point[1] = min_p_[1] + (max_p_[1] - min_p_[1]) * y / res_y_;
00073         point[2] = min_p_[2] + (max_p_[2] - min_p_[2]) * z / res_z_;
00074 
00075         PointNT p;
00076         p.getVector3fMap () = point;
00077 
00078         tree_->nearestKSearch (p, 1, nn_indices, nn_sqr_dists);
00079 
00080         grid_[x * res_y_*res_z_ + y * res_z_ + z] = input_->points[nn_indices[0]].getNormalVector3fMap ().dot (
00081             point - input_->points[nn_indices[0]].getVector3fMap ());
00082       }
00083 }
00084 
00085 
00086 
00087 #define PCL_INSTANTIATE_MarchingCubesHoppe(T) template class PCL_EXPORTS pcl::MarchingCubesHoppe<T>;
00088 
00089 #endif    // PCL_SURFACE_IMPL_MARCHING_CUBES_HOPPE_H_
00090 


pcl
Author(s): Open Perception
autogenerated on Mon Oct 6 2014 03:15:38